Data can be stored across a plurality of databases. In some examples, each database can store data in a respective data schema. Data schemas can be disparate between databases. Cross-schema and cross-database data access is a long-established problem domain, and has led to a set of tools collectively providing extraction, transformation and loading (ETL) functionality. Extraction can be defined as a process for retrieving data from a source or set of sources. Transformation can refer to data manipulation such as reformatting, error correction, normalization of information, and the like. Loading can be defined as the delivery of outputs to a target database or set of databases.
A number of products and solutions exist for ETL in conventional on-premise landscapes. An on-premise landscape can include applications and/or data sources that are local to an entity (e.g., an enterprise). For example, an on-premise application is a computer-executable application that is locally executed using computing devices that are operated by the enterprise (e.g., a company). Existing ETL solutions can require bespoke configuration, maintenance and operation.
A direct replication to an on-demand landscape, or cloud space, is considered sub-optimal due to potentially low levels of service utilization, and the high resource costs of providing large volumes of frequently redundant servers. An on-demand landscape can include applications and/or data sources that are hosted by a third-party service provider (e.g., a cloud service provider). For example, an on-demand application is a computer-executable application that is hosted on a platform provided by a service provider and that is remotely accessed by one or more entities (e.g., enterprises).